Revolutionary Vision-Prompt System Transforms AI Video Generation with Local AI Precision

Local Vision AI Meets Cinematic Prompt Engineering in Groundbreaking Beta Tool

A new open-source tool, LTX-2 EASY PROMPT v2 + VISION Node, is generating significant buzz among AI video creators for its ability to eliminate the ambiguity that has long plagued text-to-video generation. Developed by an independent researcher under the username /u/WildSpeaker7315, the system leverages a locally run vision model—Qwen2.5-VL-3B (or Qwen 7B for enhanced accuracy)—to analyze a user’s input image and generate a precise, structured scene description. This description then becomes the immutable foundation for an LLM-powered prompt generator, ensuring that every generated video frame adheres exactly to the visual reality of the source material.

Unlike conventional AI video tools that rely on vague textual prompts and risk hallucinating subject details, lighting, or composition, this system removes human error and model guesswork. As described in the Reddit post, the vision node dissects the input image across 10 key dimensions: visual style, subject attributes (age, gender, skin tone), clothing or nudity, pose, interaction objects, shot type, camera angle, lighting conditions, and background setting. Crucially, the vision model unloads from VRAM immediately after analysis, freeing up computational resources for the LTX-2 video generator without memory overhead.

The second phase involves the Easy Prompt node, which ingests the vision-generated context and transforms user instructions—such as “she slowly turns to face the camera and smiles”—into a fully cinematic prompt. The system doesn’t invent details; it animates them. This paradigm shift ensures that even complex scenes with multiple subjects are tracked accurately, with each actor’s position and action preserved across frames. The tool also supports numbered action sequences (e.g., “1. stands / 2. walks to window / 3. looks out”), preserving exact choreography without reordering or merging steps.

Additional innovations include an automated negative prompt generator that detects context (indoor/outdoor, explicit content, shot type) and applies optimized negative tags without requiring a second LLM call. This eliminates the need for manual negative prompt engineering, a time-consuming and error-prone process in traditional workflows. A LoRA trigger word input ensures consistent model activation, while a dialogue toggle allows users to choose between natural, novel-style spoken dialogue (with attribution and delivery cues) or silent generation based on user-provided quotes—critical for audio-synced video production.

For advanced users, a bypass/direct mode allows text to be sent directly to the encoder with zero LLM processing, offering full manual control at zero VRAM cost. This dual-mode architecture makes the tool accessible to both beginners seeking automation and experts demanding precision.

Support for explicit content without euphemisms, multi-subject tracking, and frame-count-aware pacing further distinguish this system. For example, a 10-second clip will generate only 2–3 distinct actions, avoiding the overcrowded, chaotic motion common in other AI video tools. The developer, who tested the system for seven hours in a single day, emphasized the tool’s stability and the physical toll of its development—“my eyes hurt bro”—a candid testament to the intensity of independent innovation in AI.

Available on GitHub and Google Drive, the beta release is already being adopted by indie animators, AI filmmakers, and content creators frustrated with the inconsistency of commercial video AI platforms. While still in early development, LTX-2 EASY PROMPT v2 represents a significant leap toward deterministic, visually faithful AI video generation—one grounded not in cloud-based speculation, but in local, accurate, and transparent analysis.